The present invention relates to the electrical, electronic and computer arts, and, more particularly, to virtual network performance assessment.
Software defined networking (SDN) offers agility, speed and cost savings in cloud deployment. The quality of service (QoS) provided by these virtual software defined networks, such as those deployed within a cloud infrastructure, impact the Quality of Experience (QoE) of the applications. To achieve a high level of QoE for applications deployed over virtual networks, it is necessary to first assess the networking capabilities of the virtual network and then identify and address areas and/or components requiring improvements.
Furthermore, to measure the performance impact of changes made to the virtual network software, it is necessary to quantitatively assess and track the networking performance across different software versions. However, performance assessment of virtual networking is a challenging for several reasons.
By way of example, a virtual network is typically composed of many components both hardware and software. Each of these components can be assessed individually based on custom metrics. However, to evaluate virtual networking from the QoE perspective of applications deployed over it, it is often necessary to quantitatively determine how all of the individual components interact.
As another example, the underlying infrastructure may be heterogeneous. Heterogeneity complicates performance evaluation because it makes the absolute performance of the virtual network essentially meaningless as a metric. For example, achieving 100 Mbps throughput is great in the case where the underlying Ethernet runs at 100 Mbps but lousy when the underlying network runs at 10 Gbps.
Also, many different types of network measurements can be collected in a cloud. Unfortunately, not all of them may provide insights to the networking performance experienced by application traffic.
Given the different sizes of clouds, diverse capabilities of individual cloud components, and heterogeneity in the infrastructure, combining measurements and metrics into meaningful scores for virtual network assessment remains a difficult problem. Thus, there is a long-felt unmet need for a solution to quantitatively assess the networking capabilities of an arbitrary heterogeneous software defined virtual network.